| Title | Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Nath, Anubhav, Biswas, Reetam Sen, Pal, Anamitra |
| Conference Name | 2019 North American Power Symposium (NAPS) |
| Keywords | additive instrumentation errors, composability, Cyber-physical systems, Databases, DSA scheme, dynamic security assessment (DSA), erroneous measurements, grid security assessment, IEEE-118 bus system, large-scale blackouts, learning (artificial intelligence), Load modeling, machine learning, machine learning (ML), measurement errors, Metrics, online dynamic security assessment, phasor measurement, phasor measurement unit (PMU), phasor measurement unit data, phasor measurement units, PMU data, PMU measurements, power engineering computing, power grids, power system control, power system measurement, power system security, power system stability, pubcrawl, Renewable Generation, Resiliency, seasonal load profiles, security, security prediction, synchrophasor technology, Transient analysis, Voltage measurement |
| Abstract | Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme. |
| DOI | 10.1109/NAPS46351.2019.9000333 |
| Citation Key | nath_application_2019 |
Application of Machine Learning for Online Dynamic Security Assessment in Presence of System Variability and Additive Instrumentation Errors